Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Agrawal, Prasun Kumar; Pandit, Manjaree; Dubey, Hari Mohan

doi:10.5815/ijisa.2016.11.05

Cited by 5 publications

(4 citation statements)

References 49 publications

(63 reference statements)

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“…The reasons for filling up these majors are mainly based on the reports of advanced technology and good development prospects of these industries by various social media. In contrast, the logistics industry seldom publicizes the bright future of the industry, and the application of intelligent logistics technology mostly stays at the level of enterprise cognition [4]. Many students unilaterally believe that "logistics is equivalent to courier delivery", and there are fewer volunteers to fill in this major.…”

Section: There Is a Gap Between Apprenticeship Quality And Enterprise Expectationmentioning

confidence: 99%

Exploration and Research on Joint Training and Education of Logistics Elite Apprentices by College and Enterprise

Ren¹

2019

dtssehs

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The author investigates the implementation of the modern apprenticeship project of logistics management major in domestic higher vocational colleges, summarizes the problems existing in the implementation process, such as the quality of apprenticeship sources, the orientation of talent training objectives, professional teaching, apprenticeship training platform, apprenticeship career, enterprise teachers and so on, and makes a thorough analysis of the causes of the problems. Taking the exploration of training logistics elite apprentices in Hubei Communications Technical College as an example, it puts forward the precise orientation of the training objectives of logistics elite apprenticeship, implements the joint enrollment, joint training and joint employment of college and enterprise, and standardizes the selection mechanism of enterprise teachers to improve the quality of apprenticeship training. These practices of training logistics elite apprentices have been popularized and applied in other higher vocational colleges, and have achieved good results.

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Section: There Is a Gap Between Apprenticeship Quality And Enterprise Expectationmentioning

confidence: 99%

Exploration and Research on Joint Training and Education of Logistics Elite Apprentices by College and Enterprise

Ren¹

2019

dtssehs

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“…-Very few control variables (Mukherjee and Mukherjee 2016;). -Good balance between global and local search (Agrawal, Pandit, and Dubey 2016). -Few parameters to regulate (Wang, Hossein Gandomi, and Hossein Alavi 2013).…”

Section: A Comparison Of the Proposed Population-based Metaheuristicsmentioning

confidence: 99%

A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

Chansombat

Musikapun

Pongcharoen

et al. 2018

International Journal of Production Research

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Capital goods companies produce high value products such as power plant or ships, which have deep and complex product structures, with components having long process routings. Contracts usually include substantial penalties for late delivery. The high value of items can lead to substantial holding costs. Efficient schedules minimise earliness and tardiness costs and need to satisfy assembly and operation precedence constraints as well as finite capacity. This paper presents the first advanced planning and scheduling (APS) tool for the capital goods industry that uses a Discrete Bat Algorithm (DBA), modified DBA (MDBA) and hybrid DBA with Krill Herd algorithm (HDBK) to optimise schedules. The tool was validated using four datasets obtained from a collaborating capital goods company. A sequential experimental strategy was adopted. The first experiment identified appropriate parameter settings for the DBA. The second experiment evaluated and compared the performance of the proposed HDBK algorithm with an Artificial Bee Colony, Krill Herd (KH), Modified KH, DBA and MDBA metaheuristics. The experimental results revealed that the HDBK performed best in terms of the minimum penalty cost for all problem sizes and achieved up to a 47.837% reduction in mean total penalty costs of extra-large problem size.

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“…, * ) ∈ verifying ( * ) = min ∈ ( ). There are in general, many fields of swarm approach application in resolving combinatorial optimization problems [7][8][9][10][11], and variants of ant colony algorithms, in neural network [12], telecommunication network [13], computer science engineering [14][15], robotic [16], energetic efficiency [17], and other general fields [18][19].…”

Section: A Combinatorial Optimization Problems (Cop)mentioning

confidence: 99%

A New Approach for Dynamic Parametrization of Ant System Algorithms

Masrour¹,

Rhazzaf²

2018

IJISA

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Abstract-This paper proposes a learning approach for dynamic parameterization of ant colony optimization algorithms. In fact, the specific optimal configuration for each optimization problem using these algorithms, whether at the level of preferences, the level of evaporation of the pheromone, or the number of ants, makes the dynamic approach an interested one. The new idea suggests the addition of a knowledge center shared by the colony members, combining the optimal evaluation of the configuration parameters proposed by the colony members during the experiments. This evaluation is based on qualitative criteria explained in detail in the article. Our approach indicates an evolution in the quality of the results over the course of the experiments and consequently the approval of the concept of machine learning.

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Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Cited by 5 publications

References 49 publications

Exploration and Research on Joint Training and Education of Logistics Elite Apprentices by College and Enterprise

Exploration and Research on Joint Training and Education of Logistics Elite Apprentices by College and Enterprise

A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

A New Approach for Dynamic Parametrization of Ant System Algorithms

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